return %2 : tensor<?x2x2x1xi32>
}

// -----

// CHECK-LABEL: func @convolution_add_add_static
func.func @convolution_add_add_static(
    %lhs: tensor<2x3x2x1xi8>, %rhs: tensor<2x1x1x1xi8>,
    %zp_offset: tensor<2x2x2x1xi32>, %bias: tensor<1xi32>
  ) -> tensor<2x2x2x1xi32> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution
  // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]

    %8 = stablehlo.constant dense<-5> : tensor<1x1x1xi8>  // Output zero point (z3).
    %9 = stablehlo.constant dense<1.250000e+01> : tensor<1x1x1xf32>  // Merged scale (s1 * s2).
    %10 = call @uniform_quantize(%arg0, %1, %2) : (tensor<8x16x16xf32>, tensor<1x1x1xf32>, tensor<1x1x1xi8>) -> tensor<8x16x16xi8>  // q1
    %11 = call @uniform_quantize_0(%arg1, %4, %5) : (tensor<8x16x4xf32>, tensor<1x1x1xf32>, tensor<1x1x1xi8>) -> tensor<8x16x4xi8>  // q2

// PerChannel-DAG: %[[bias1:.*]] = "tf.Const"() <{value = dense<0.000000e+00> : tensor<3xf32>}> : () -> tensor<3xf32>
// PerChannel-DAG: %[[q_w1:.*]] = "tf.Const"() <{value = dense<{{[0-9]+}}> : tensor<2x3x3x1xi8>}> : () -> tensor<2x3x3x1xi8>
// PerChannel-DAG: %[[q_w2:.*]] = "tf.Const"() <{value = dense<{{[0-9]+}}> : tensor<2x3x3x2xi8>}> : () -> tensor<2x3x3x2xi8>

    %1 = "tf.Sub"(%0, %arg4) : (tensor<1x3x4x3xi32>, tensor<i32>) -> tensor<1x3x4x3xi32>
    %identity = "tf.Identity"(%arg1) : (tensor<2x3x3x1xi8>) -> tensor<2x3x3x1xi8>
    %2 = "tf.Cast"(%identity) {Truncate = false} : (tensor<2x3x3x1xi8>) -> tensor<2x3x3x1xi32>
    %3 = "tf.Sub"(%2, %arg6) : (tensor<2x3x3x1xi32>, tensor<1xi32>) -> tensor<2x3x3x1xi32>

}

TEST_F(AttrsAndConstraintsTest, HasRankOfReturnsTrueForMatchingRank) {
  constexpr absl::string_view kConstantOpWithRankFour =
      R"mlir(%0 = stablehlo.constant dense<0> : tensor<1x1x1x1xi8>)mlir";
  OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kConstantOpWithRankFour);
  ASSERT_TRUE(module_op);

  ASSERT_FALSE(module_op->getBodyRegion().empty());

  %1 = "tfl.reshape"(%0, %cst) : (tensor<2x2x1x16x!quant.uniform<i8<-127:127>:f32, 0.022395913056501255>>, tensor<4xi32>) -> tensor<1x2x2x16x!quant.uniform<i8<-127:127>:f32,...

func.func @conv_with_bias_same_padding_srq_depthwise(%arg0: tensor<1x4x5x3x!quant.uniform<i8:f32, 2.000000e+00:0>>) -> (tensor<1x5x6x3x!quant.uniform<i8:f32, 8.000000e+00:-128>>) {
  %0 = stablehlo.constant() {value = dense<3> : tensor<2x2x1x3xi8>} : () -> tensor<2x2x1x3x!quant.uniform<i8:f32:3, {3.000000e+00, 3.000000e+00, 3.000000e+00}>>